2025-05-20
In 2018, The Economist named her one of the decade’s eight best young economists.
In 2014 she was named by the IMF as the youngest of 25 economists under the age of 45 shaping thought about the global economy.
Three empirical papers on ESG and financial markets:
ESG Disclosure and Price Informativeness
→ Do disclosures improve market efficiency?
ESG in Asset Pricing with Deep Learning
→ Do ESG features improve stock return forecasts?
Institutional ESG Consistency
→ Do institutions align ESG rhetoric with their actions?
All papers combine machine learning and causal inference with financial and text data.
Main Goal:
To examine whether ESG disclosures improve the efficiency of financial markets.
Key Questions:
- Do ESG disclosures lead to lower return volatility?
- Do they reduce bid-ask spreads?
- Do they accelerate price discovery?
Why It Matters:
- ESG reporting is increasing but varies in quality.
- Prior studies focus on the presence of disclosure, not the content.
- Investors and regulators need to know what kind of ESG reporting actually informs markets.
Expected Results:
- Forward-looking, specific ESG disclosures will reduce:
Why?
- Christensen, Serafeim, & Sikochi (2022):
High-quality ESG disclosures improve liquidity and transparency.
→ Content quality, not just disclosure presence, drives outcomes.
Main Goal:
To test whether ESG features improve stock return prediction in a machine learning framework.
Key Questions:
- Can ESG data improve asset pricing models?
- Are ESG-informed portfolios more profitable and stable?
Why It Matters:
- Traditional models (e.g., Fama-French) assume linearity.
- ESG data is high-dimensional, likely nonlinear, and underused.
- ML has improved pricing models (Gu et al., 2020) but rarely includes ESG.
Expected Results:
- ESG features will enhance predictive power
- ESG-augmented models will yield:
- Higher Sharpe ratios
- Better R²
- Lower turnover
Why?
- Gu, Kelly, & Xiu (2020):
ML models outperform linear ones in return prediction.
→ Adding ESG may reveal alpha related to risk or preferences.
Main Goal:
To assess whether institutional investors align ESG claims with their real behavior.
Key Questions:
- Do ESG statements match actual voting and portfolio decisions?
- Do inconsistencies affect investor flows?
Why It Matters:
- ESG credibility is under fire — greenwashing is a top concern
- No scalable method exists to measure ESG consistency
- Investor trust depends on aligning talk and action
Expected Results:
- Many funds will show inconsistencies across statements, votes, and holdings
- Inconsistent funds will lose capital from ESG-conscious investors
Why?
- Raghunandan & Rajgopal (2022):
ESG-branded funds often vote against ESG proposals.
→ This disconnect signals symbolic rather than substantive commitment.